# Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish

Source: [Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnish](<https://www.sota2.com/research/paper/6a17cc4ddbd7150506f81569>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-29T05:00:12.873Z

Authors: Yongseok Jang

Published: 2026-05-27T13:41:32.000Z

## Abstract

Composing finite-state transducers (FSTs) for context-dependent morphophonological rules -- consonant gradation, vowel harmony, possessive suffix assimilation -- leads to multiplicative state explosion; neural models sidestep the problem but provide no formal account of the rules themselves. We present the first framework where each morphophonological rule is a function from a focused local context to a single output segment -- the type of a local rule familiar from cellular automata -- and where length-changing rules compose as coKleisli arrows of a comonad. Our central contribution is the Writer comonad (DeletionSet x Zipper), a new algebraic construction that restores strict coKleisli compositionality for such rules: each rule is a coKleisli arrow, extend lifts it to a global transformation, and deletions accumulate as a monoid action rather than requiring intermediate materialization. As supporting evidence, thirteen coKleisli arrows provide an alternative formulation expressing the same morphophonological behaviors that Omorfi encodes via 874 continuation classes (67:1 reduction at the rule-representation level), and the same abstraction enables bidirectional morphology -- a MorphGenerator reuses the analysis arrows for generation. On UD Finnish-TDT, the system achieves 83.92% UPOS accuracy with rule-only disambiguation (94.66% with an external suffix tagger), validating the framework as a practical morphological engine.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Lemmatization on UD Finnish-TDT (dev)](<https://www.sota2.com/research/sota/lemmatization-on-ud-finnish-tdt-dev.md>): Lemmatization; UD Finnish-TDT (dev); Lemma Accuracy: 93.09.

- [Part-of-Speech Tagging on UD Finnish-TDT (dev)](<https://www.sota2.com/research/sota/part-of-speech-tagging-on-ud-finnish-tdt-dev.md>): Part-of-Speech Tagging; UD Finnish-TDT (dev); UPOS Accuracy: 94.66.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.28484>)

[PDF](<https://arxiv.org/pdf/2605.28484.pdf>)